class_docstr: AVA Dataset
class_docstr_tok: ["AVA","Dataset"]
code: def __init__(self, cfg, split):
      self.cfg = cfg
      self._split = split
      self._sample_rate = cfg.DATA.SAMPLING_RATE
      self._video_length = cfg.DATA.NUM_FRAMES
      self._seq_len = self._video_length * self._sample_rate
      self._num_classes = cfg.MODEL.NUM_CLASSES
      # Augmentation params.
      self._data_mean = cfg.DATA.MEAN
      self._data_std = cfg.DATA.STD
      self._use_bgr = cfg.AVA.BGR
      self.random_horizontal_flip = cfg.DATA.RANDOM_FLIP
      if self._split == "train":
          self._crop_size = cfg.DATA.TRAIN_CROP_SIZE
          self._jitter_min_scale = cfg.DATA.TRAIN_JITTER_SCALES[0]
          self._jitter_max_scale = cfg.DATA.TRAIN_JITTER_SCALES[1]
          self._use_color_augmentation = (
              cfg.AVA.TRAIN_USE_COLOR_AUGMENTATION
          )
          self._pca_jitter_only = cfg.AVA.TRAIN_PCA_JITTER_ONLY
          self._pca_eigval = cfg.DATA.TRAIN_PCA_EIGVAL
          self._pca_eigvec = cfg.DATA.TRAIN_PCA_EIGVEC
      else:
          self._crop_size = cfg.DATA.TEST_CROP_SIZE
          self._test_force_flip = cfg.AVA.TEST_FORCE_FLIP
  
      self._load_data(cfg)
  
file_path: SlowFast/slowfast/datasets/ava_dataset.py
imports: import logging
  import numpy
  import torch
  from .None import ava_helper
  from .None import cv2_transform
  from .None import transform
  from .None import utils
  from .build import DATASET_REGISTRY
label_desc: Use image node_id 1 to create a new Ava object from inherited base classes: torch.utils.data with example: obj = Ava(cfg, split)